Implementing Real-Time Wait Time Estimates in Genesys Cloud CX Architect Flows

How-To-Articles
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Implementing Real-Time Wait Time Estimates in Genesys Cloud CX Architect Flows

What This Guide Covers

This guide details the configuration and architectural considerations for displaying accurate, real-time wait time estimates to callers within Genesys Cloud CX Architect flows. The end result is an IVR experience where callers are informed of estimated wait times before being placed in a queue, improving caller experience and potentially reducing abandonment rates.

Prerequisites, Roles & Licensing

  • Licensing Tier: Genesys Cloud CX 2.0 or higher. Real-time metrics are available in CX 2.0 and above, and the underlying Architect functionality is standard.
  • Permissions: The user configuring this requires the following permissions:
    • Architect > Flow > View
    • Architect > Flow > Edit
    • Architect > Property > View
    • Reporting > Historical Reporting > View (to verify metric data)
  • OAuth Scopes: Not applicable for core configuration, but necessary for any API interactions to supplement wait time data.
  • External Dependencies: Accurate queue statistics are fundamental. Ensure queues are properly configured and receiving expected call volume.

The Implementation Deep-Dive

1. Configuring the Queue Metrics Data Source

The foundation of real-time wait time estimates relies on the accurate reporting of queue metrics. Genesys Cloud CX provides these natively; we will leverage them within the Architect flow. The core metric we’ll use is Queue.CurrentWaitTime. This metric, however, presents challenges; it is a point-in-time measurement and can be volatile. We need to smooth it. We’ll accomplish smoothing using a moving average calculated within the flow.

Navigate to Admin > Contact Center > Queues. Select the target queue. Ensure the queue is actively reporting metrics. Confirm Enable Real-Time Metrics is enabled. This setting is on by default, but it’s the first point of failure.

The Trap: Forgetting to enable Enable Real-Time Metrics on the queue. This results in the Architect flow receiving zero data for the Queue.CurrentWaitTime metric, causing a default “0 minutes” wait time announcement.

2. Building the Architect Flow - Data Retrieval and Calculation

We will construct an Architect flow designed to retrieve the Queue.CurrentWaitTime metric, apply a smoothing algorithm, and then present the data to the caller. This will be a sub-routine that can be re-used.

  1. Create a New Flow: Navigate to Architect > Flows and create a new flow named “GetWaitTimeEstimate”.
  2. Add a “Get Queue Metrics” Node: Drag and drop a “Get Queue Metrics” node into the flow.
    • Queue ID: Select the target queue.
    • Metrics: Select Queue.CurrentWaitTime.
  3. Add a “Set Variable” Node: Drag and drop a “Set Variable” node.
    • Variable Name: v_rawWaitTime
    • Variable Type: Integer
    • Value: $Queue.CurrentWaitTime
  4. Add a “Set Variable” Node (Smoothing Algorithm): This is where we apply the smoothing. We will implement a simple moving average over the last 3 samples. This requires 3 v_rawWaitTime variables to store previous values: v_waitTimeSample1, v_waitTimeSample2, v_waitTimeSample3.
    • Variable Name: v_smoothedWaitTime
    • Variable Type: Integer
    • Value: (($Queue.CurrentWaitTime + $v_waitTimeSample1 + $v_waitTimeSample2) / 3).round()
  5. Add a “Set Variable” Node (Shift Samples): Shift the samples to prepare for the next iteration.
    • Variable Name: v_waitTimeSample3
    • Variable Type: Integer
    • Value: $v_waitTimeSample2
  6. Add a “Set Variable” Node:
    • Variable Name: v_waitTimeSample2
    • Variable Type: Integer
    • Value: $v_waitTimeSample1
  7. Add a “Set Variable” Node:
    • Variable Name: v_waitTimeSample1
    • Variable Type: Integer
    • Value: $v_rawWaitTime
  8. Add a “Play Audio” or “Text to Speech” Node: This node will announce the wait time to the caller.
    • Audio/Text: Construct a message like: “Your estimated wait time is [v_smoothedWaitTime] minutes.”
  9. Return to Calling Flow: Add a “Transfer” or “Disconnect” node to return the call to the original flow.

The Trap: Using $Queue.CurrentWaitTime directly without smoothing. This will result in a fluctuating wait time estimate that feels erratic and unreliable to the caller, often being lower than the actual wait time. The moving average provides a more stable and realistic estimation.

3. Integrating the Flow into the Main IVR Flow

Now, integrate the “GetWaitTimeEstimate” flow into your primary IVR flow before transferring the caller to the target queue.

  1. Locate the Transfer Node: Find the “Transfer” node in your IVR flow that sends the caller to the target queue.
  2. Add a “Sub-Flow” Node: Insert a “Sub-Flow” node immediately before the “Transfer” node.
  3. Configure the Sub-Flow Node:
    • Flow: Select the “GetWaitTimeEstimate” flow you created.
    • Pass Context Data: No context data needs to be passed. The flow will directly access the queue metrics.

Validation, Edge Cases & Troubleshooting

Edge Case 1: Queue is Empty

  • The Failure Condition: The queue is empty, resulting in a Queue.CurrentWaitTime of 0. The smoothed wait time will also be 0, leading to an announcement of “Your estimated wait time is 0 minutes.” This can be misleading if callers are still experiencing delays due to other factors.
  • The Root Cause: The metric accurately reflects an empty queue, but the messaging is not nuanced.
  • The Solution: Add conditional logic to the “Play Audio” node in the “GetWaitTimeEstimate” flow. If v_smoothedWaitTime is less than 1, announce a message like “All agents are currently assisting other customers. Your wait time may vary.”

Edge Case 2: High Call Volume Spikes

  • The Failure Condition: A sudden spike in call volume causes the Queue.CurrentWaitTime to increase dramatically. The smoothed wait time, while mitigating the immediate jump, may still lag behind the actual perceived wait time, especially with a small sample size (3 in this case).
  • The Root Cause: The smoothing algorithm cannot instantly react to rapidly changing conditions.
  • The Solution: Increase the sample size in the smoothing algorithm (e.g., use 5 or 7 previous samples), or implement a weighted moving average that gives more weight to recent samples. However, be cautious about increasing the sample size too much, as it will reduce responsiveness.

Edge Case 3: Metric Reporting Latency

  • The Failure Condition: A delay in metric reporting causes stale data to be used for the wait time estimate. This will lead to inaccurate announcements.
  • The Root Cause: Genesys Cloud reporting isn’t instantaneous. There is a small delay.
  • The Solution: This is difficult to solve definitively. Monitor the reporting latency and adjust the frequency of the “Get Queue Metrics” node if possible. Consider adding a disclaimer to the announcement: “Estimated wait times are subject to change.”

Official References